Breaking the link – how robust are gene expression networks?

The intricate biological cascades that fine-tune cellular protein production are hugely complex – and so is the task of deciphering them. We found out more about a new technique developed in the Fulga lab to disentangle this regulatory web.

The complex regulatory layers that control when, where and how much protein is produced by cells, can be thought of much like the London Underground system. Both systems are highly efficient (when they are working well, often not at the weekend) and form complex networks, which are fairly robust to changes such as the closure of a station or a line by diverting through other routes.

Most experimental techniques to analyse gene expression regulatory networks rely on deletion of a particular component, akin to closure of a station. This affects all of the lines that enter and leave that station, and often causes knock-on effects on other stations because of the necessity to find alternative routes. In extreme cases this can result in a complete rewiring or collapse of the entire network. If you are looking at the system as a whole, this makes it difficult to disentangle the primary effects of the closure from other secondary consequences.

In a study published recently in Nature Communications, an international team led by postdoctoral fellow Qianxin Wu, PhD student Quentin Ferry and Associate Professor Tudor Fulga in the MRC WIMM (Radcliffe Department of Medicine), in collaboration with Dr Andrew Bassett of the Dunn School of Pathology (presently head of research in cellular operations at the Wellcome Trust Sanger Institute), developed a CRISPR/Cas9 genome engineering platform (termed GenERA) that allows precise removal of specific links in a gene expression network, analogous to closure of specific lines between two stations, rather than the stations themselves. This makes it possible to understand the importance of these links in maintaining the functionality of the network, without perturbing the system homeostasis. Additionally, it allows researchers to uncover sequence codes that are important for regulating RNA stability or processing without prior knowledge, as well as to validate known or predicted codes in their native context within the cell.

The Oxford team used this technology to understand the activity of microRNAs, small RNA molecules that form an important regulatory layer of gene expression. They act by controlling the abundance of a large number of messenger RNA targets within the cell. The set of targets depends on a short region of similarity in sequence with the microRNA, allowing researchers to search for these sequences using computer algorithms. However, this often predicts hundreds or even thousands of targets for each microRNA. With a total of more than 1000 microRNAs in humans, this forms a highly complex network, and it is thought that nearly all of the tens of thousands of RNA molecules in a cell could at some point in time be regulated in this manner. However, understanding the consequence of this regulation at the level of entire networks remains one of the most important, yet elusive, dimensions in miRNA biology.

By applying the GenERA technology to the entire set of predicted targets for one microRNA, Wu and her colleagues were able to specifically remove individual links in this complex network within a living cell, allowing them to interrogate the importance of each microRNA-target interaction. Their results revealed that the majority of predicted microRNA targets are functional, but the extent to which they alter output RNA levels differs dramatically between targets. The team also removed combinations of different links and demonstrated that whilst the network can withstand the removal of a single link, deletion of multiple links can result in a far more dramatic effect.

This uniquely versatile technology can be now used to investigate hundreds or even thousands of microRNA-target links in a single experiment, and can be more broadly applied to study or discover other RNA regulatory elements across the genome. This will help decipher the importance of such elements both during normal organismal development as well as in pathological situations, allowing researchers to draw more accurate maps of RNA regulatory networks. These insights will be instrumental for devising strategies aiming to manipulate such networks for therapeutic or biotechnological benefit.